CASTLE: Continuously anonymizing data streams

IEEE Transactions on Dependable and Secure Computing

Volumn 8, Issue 3, 2011, Pages 337-352

  Cao, Jianneng ^a   Carminati, Barbara ^b   Ferrari, Elena ^b   Tan, Kian Lee ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b UNIVERSITY OF INSUBRIA   (Italy)

Author keywords

anonymity; Data stream; privacy preserving data mining

Indexed keywords

DATA MINING;

ADAPTIVE CLUSTERING; ANONYMITY; DATA STREAM; DELAY CONSTRAINTS; PERFORMANCE STUDY; PRIVACY PRESERVING; PRIVACY PRESERVING DATA MINING; STREAMING APPLICATIONS;

DATA PRIVACY;

EID: 79952842019     PISSN: 15455971     EISSN: None     Source Type: Journal    
DOI: 10.1109/TDSC.2009.47     Document Type: Article

References (32)

1. R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules in Large Databases, " Proc. Int'l Conf. Very Large Databases (VLDB), pp. 478-499, 1994.
2. R. Agrawal and R. Srikant, "Privacy-Preserving Data Mining, " Proc. SIGMOD, pp. 439-450, 2000.
3. C. C. Aggarwal, "On k-Anonymity and the Curse of Dimensionality, " Proc. Int'l Conf. Very Large Databases (VLDB), pp. 901-909, 2005. (Pubitemid 43991062)
4. C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu, "A Framework for Clustering Evolving Data Streams, " Proc. Int'l Conf. Very Large Databases (VLDB), pp. 81-92, 2003.
5. C. C. Aggarwal and P. S. Yu, "A Condensation Approach to Privacy Preserving Data Mining, " Proc. Int'l Conf. Extending Database Technology (EDBT), pp. 183-199, 2004.
6. G. Aggarwal, T. Feder, K. Kenthapadi, S. Khuller, R. Panigrahy, D. Thomas, and A. Zhu, "Achieving Anonymity via Clustering, " Proc. Symp. Principles of Database Systems (PODS), pp. 153-162, 2006. (Pubitemid 46946477)
7. M. Atzori, "Weak k-Anonymity: A Low-Distortion Model for Protecting Privacy, " Proc. Int'l Security Conf., pp. 60-71, 2006. (Pubitemid 44609938)
8. R. J. Bayardo and R. Agrawal, "Data Privacy through Optimal k-Anonymization, " Proc. Int'l Conf. Data Eng. (ICDE), pp. 217-228, 2005. (Pubitemid 41731133)
9. J. W. Byun, Y. Sohn, E. Bertino, and N. Li, "Secure Anonymization for Incremental Data Sets, " Proc. Very Large Databases (VLDB) Workshop Secure Data Management, pp. 48-63, 2006. (Pubitemid 44699622)
10. J. W. Byun, A. Kamra, E. Bertino, and N. Li, "Efficient k-Anonymization Using Clustering Techniques, " Proc. Database Systems for Advanced Applications (DASFAA), pp. 188-200, 2007.
11. J. Cao, B. Carminati, E. Ferrari, and K. L. Tan, "CASTLE: A Delay-Constrained Scheme for k s-Anonymizing Data Streams, " Proc. Int'l Conf. Data Eng. (ICDE), Poster Paper, pp. 1376-1378, 2008.
12. P. Domingos and G. Hulten, "Mining High-Speed Data Streams, " Proc. Int'l Conf. Knowledge Discovery and Data Mining (KDD), pp. 71-80, 2000.
13. P. Zhang, X. Zhu, and Y. Shi, "Categorizing and Mining Concept Drifting Data Streams, " Proc. Int'l Conf. Knowledge Discovery and Data Mining (KDD), pp. 812-820, 2008.
14. C. Luo, H. Thakkar, H. Wang, and C. Zaniolo, "A Native Extension of SQL for Mining Data Streams, " Proc. SIGMOD, pp. 873-875, 2005. (Pubitemid 43038994)
15. J. Domingo-Ferrer and V. Torra, "Ordinal, Continuous and Heterogeneous k-Anonymity through Microaggregation, " Data Mining and Knowledge Discovery, vol. 11, no. 2, pp. 195-212, 2005. (Pubitemid 41476765)
16. J. Domingo-Ferrer, F. Sebe, and A. Solanas, "A Polynomial-Time Approximation to Optimal Multivariate Microaggregation, " Computers and Math. with Applications, vol. 55, no. 4, pp. 714-732, 2008.
17. S. Guha, N. Mishra, R. Motwani, and L. O'Callaghan, "Clustering Data Streams, " Proc. IEEE Symp. Foundations of Computer Science (FOCS), pp. 359-366, 2000. (Pubitemid 32075905)
18. B. C. M. Fung, K. Wang, and P. S. Yu, "Top-Down Specialization for Information and Privacy Preservation, " Proc. Int'l Conf. Data Eng. (ICDE), pp. 205-216, 2005. (Pubitemid 41731132)
19. V. S. Iyengar, "Transforming Data to Satisfy Privacy Constraints, " Proc. Int'l Conf. Knowledge Discovery and Data Mining (KDD), pp. 279-288, 2002.
20. S. Kim and W. Winkler, "Masking Microdata Files, " Proc. Section on Survey Research Methods, pp. 114-119, 1995.
21. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan, "Incognito: Efficient Full-Domain k-Anonymity, " Proc. SIGMOD, pp. 49-60, 2005. (Pubitemid 43038916)
22. K. LeFevre, D. J. DeWitt, and R. Ramakrishnan, "Mondrian Multidimensional K-Anonymity, " Proc. Int'l Conf. Data Eng. (ICDE), p. 25, 2006. (Pubitemid 44539817)
23. N. Li and T. Li, "t-Closeness: Privacy beyond k-Anonymity and'-Diversity, " Proc. Int'l Conf. Data Eng. (ICDE), pp. 106-115, 2007.
24. A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam, "L-Diversity: Privacy beyond k-Anonymity, " Proc. Int'l Conf. Data Eng. (ICDE), p. 24, 2006.
25. P. Samarati and L. Sweeney, "Generalizing Data to Provide Anonymity When Disclosing Information, " Proc. Symp. Principles of Database Systems (PODS), p. 188, 1998.
26. J. Pei, J. Xu, Z. Wang, W. Wang, and K. Wang, "Maintaining K-Anonymity against Incremental Updates, " Proc. Int'l Conf. Scientific and Statistical Database Management (SSDBM), p. 5, 2007.
27. L. Qiu, Y. Li, and X. Wu, "Protecting Business Intelligence and Customer Privacy While Outsourcing Data Mining Tasks, " Knowledge and Information Systems, vol. 17, no. 1, pp. 99-120, 2008.
28. L. Sweeney, "Achieving k-Anonymity Privacy Protection Using Generalization and Suppression, " Int'l J. Uncertainty, Fuzziness, and Knowledge-Based Systems, vol. 10, pp. 571-588, 2002. (Pubitemid 35384858)
29. T. M. Truta and A. Campan, "k-Anonymization Incremental Maintenance and Optimization Techniques, " Proc. ACM Symp. Applied Computing (SAC), pp. 380-387, 2007. (Pubitemid 47568319)
30. X. Xiao and Y. Tao, "M-Invariance: Towards Privacy Preserving Re-Publication of Dynamic Data Sets, " Proc. SIGMOD, pp. 689-700, 2007. (Pubitemid 47630845)
31. M. Laszlo and S. Mukherjee, "Approximation Bounds for Minimum Information Loss Microaggregation, " IEEE Trans. Knowledge and Data Eng., vol. 21, no. 11, pp. 1643-1647, Nov. 2009.
32. G. Ghinita, P. Karras, P. Kalnis, and N. Mamoulis, "Fast Data Anonymization with Low Information Loss, " Proc. Int'l Conf. Very Large Databases (VLDB), pp. 758-769, 2007.